Hypothetical Pattern Recognition Design Using Multi-Layer Perceptorn Neural Network For Supervised Learning
Md. Abdullah-al-mamun, Mustak Ahmed
Index Terms: Pattern Recognition, Multi-Layer Perceptron, MLP, Artificial Neural Network, ANN, Backpropagation, Supervised learning
Abstract: Humans are capable to identifying diverse shape in the different pattern in the real world as effortless fashion due to their intelligence is grow since born with facing several learning process. Same way we can prepared an machine using human like brain (called, Artificial Neural Network) that can be recognize different pattern from the real world object. Although the various techniques is exists to implementation the pattern recognition but recently the artificial neural network approaches have been giving the significant attention. Because, the approached of artificial neural network is like a human brain that is learn from different observation and give a decision the previously learning rule. Over the 50 years research, now a day’s pattern recognition for machine learning using artificial neural network got a significant achievement. For this reason many real world problem can be solve by modeling the pattern recognition process. The objective of this paper is to present the theoretical concept for pattern recognition design using Multi-Layer Perceptorn neural network(in the algorithm of artificial Intelligence) as the best possible way of utilizing available resources to make a decision that can be a human like performance.
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